Perhaps the most significant development on the Internet in recent years has been the rapid proliferation of online social networks, such as LinkedIn® and Facebook®. Billions of users are presently accessing such social networks to connect with friends and acquaintances and to share personal and professional information. During operation, such online social networks make millions of decisions each day, for example to determine which news articles will be interesting to specific users, to determine whether user comments constitute spam, or to determine which types of subscription offers to present to a given user. The quality of these decisions directly affects user satisfaction and subscription revenue, and is therefore critically important to the success of an online social network.
However, designing a system to make good decisions can be a challenging task because a user's preferences and associated behavior can change over time. Moreover, an online social network makes decisions for a wide variety of different purposes. Hence, it is desirable for the same decision-making methodology to be easily adaptable for these different purposes.